Data augmentation for image classification tasks

ABSTRACT

A computer-implemented method and systems are provided for performing machine learning for an image classification task. The method includes selecting, by a processor operatively coupled to one or more databases, a first and a second image from one or more training sets in the one or more databases. The method further includes overlaying, by the processor, the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image. The method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.

BACKGROUND Technical Field

The present invention relates generally to information processing and,in particular, to data augmentation for image classification tasks.

Description of the Related Art

Data augmentation is a technique used in certain applications. Ingeneral, data augmentation involves applying a small mutation totraining images for better generalization performance by avoidingoverfitting.

However, the use of data augmentation is not without deficiency. Forexample, conventional data augmentation techniques suffer from a lack ofhigh accuracy as well as unduly prolonged training time for imageclassification tasks. Accordingly, there is a need for an improved dataaugmentation technique for image classification tasks.

SUMMARY

According to an aspect of the present invention, a computer-implementedmethod is provided for performing machine learning for an imageclassification task. The method includes selecting, by a processoroperatively coupled to one or more databases, a first and a second imagefrom one or more training sets in the one or more databases. The methodfurther includes overlaying, by the processor, the second image on thefirst image to form a mixed image, by averaging an intensity of each ofa plurality of co-located pixel pairs in the first and the second image.The method also includes training, by the processor, a machine learningprocess configured for the image classification task using the mixedimage to augment data used by the machine learning process for the imageclassification task.

According to another aspect of the present invention, a computer programproduct is provided for performing machine learning for an imageclassification task. The computer program product includes anon-transitory computer readable storage medium having programinstructions embodied therewith. The program instructions are executableby a computer to cause the computer to perform a method. The methodincludes selecting, by a processor operatively coupled to one or moredatabases, a first and a second image from one or more training sets inthe one or more databases. The method further includes overlaying, bythe processor, the second image on the first image to form a mixedimage, by averaging an intensity of each of a plurality of co-locatedpixel pairs in the first and the second image. The method also includestraining, by the processor, a machine learning process configured forthe image classification task using the mixed image to augment data usedby the machine learning process for the image classification task.

According to yet another aspect of the present invention, a computerprocessing system is provided for performing machine learning for animage classification task. The computer processing system includes aprocessor that is operatively coupled to one or more databases. Theprocessor is configured to select a first and a second image from one ormore training sets in the one or more databases. The processor isfurther configured to overlay the second image on the first image toform a mixed image, by averaging an intensity of each of a plurality ofco-located pixel pairs in the first and the second image. The processoris also configured to train a machine learning process for the imageclassification task using the mixed image to augment data used by themachine learning process for the image classification task.

According to still another aspect of the present invention, an advanceddriver-assistance system is provided for a motor vehicle. The advanceddriver-assistance system includes a camera configured to capture anactual image relating to an external view from the motor vehicle. Theadvanced driver-assistance system further includes a processor that isoperatively coupled to one or more databases. The processor isconfigured to select a first and a second image from one or moretraining sets in the one or more databases. The processor is furtherconfigured to overlay the second image on the first image to form amixed image, by averaging an intensity of each of a plurality ofco-located pixel pairs in the first and the second image. The processoris also configured to perform machine learning by training a machinelearning process configured for an image classification task using themixed image to augment data used by the machine learning process for theimage classification task. The image classification task relates to adriver-assistance function. The processor is additionally configured toapply the trained machine learning process to the test image to obtain aclassification for the test image. The processor is also configured tocontrol a function of one or more hardware devices of the motor vehicle,responsive to the classification for the test image.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 shows an exemplary processing system to which the presentinvention may be applied, in accordance with an embodiment of thepresent invention;

FIG. 2 shows an exemplary system to which the present invention can beapplied, in accordance with an embodiment of the present invention;

FIGS. 3-4 show an exemplary method for data augmentation for imageclassification tasks, in accordance with an embodiment of the presentinvention;

FIGS. 5-6 show another exemplary method for data augmentation for imageclassification tasks, in accordance with an embodiment of the presentinvention;

FIG. 7 shows an overall neural network training scheme to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention;

FIG. 8 is a block diagram showing an illustrative cloud computingenvironment having one or more cloud computing nodes with which localcomputing devices used by cloud consumers communicate, in accordancewith an embodiment of the present invention; and

FIG. 9 is a block diagram showing a set of functional abstraction layersprovided by a cloud computing environment, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

The present invention is directed to data augmentation for imageclassification tasks.

In an embodiment, the present invention can achieve a higher accuracy inimage classification tasks with a Neural Network (NN) by introducing anew data augmentation technique. It is to be appreciated that thepresent invention is not limited to neural networks and can be used withany learning mechanism/technique, as readily appreciated by one ofordinary skill in the art given the teachings of the present inventionprovided herein. The present invention is not limited to any particulartype of neural network and, thus, can be used with neural network suchas Convolutional Neural Networks, Recurrent Neural Networks (RNNs), andso forth. Moreover, the present invention can also be applied to non-NNbased learning mechanisms/techniques including, but not limited to,Inductive Logic Programming (ILP), decision trees, and so forth. Theseand other learning mechanisms/techniques to which the present inventioncan be applied are readily determined by one of ordinary skill in theart, while maintaining the spirit of the present invention.

Data augmentation in accordance with the present invention can involveany mutation applied to the training images. For example, dataaugmentation in accordance with the present invention can involve, butis not limited to, overlaying noise, distorting, extraction, rotation,translation, rescaling, shearing, stretching, and flipping for eachinput image.

FIG. 1 shows an exemplary processing system 100 to which the inventionprinciples may be applied, in accordance with an embodiment of thepresent invention. The processing system 100 includes at least oneprocessor (CPU) 104 operatively coupled to other components via a systembus 102. A cache 106, a Read Only Memory (ROM) 108, a Random AccessMemory (RAM) 110, an input/output (I/O) adapter 120, a sound adapter130, a network adapter 140, a user interface adapter 150, and a displayadapter 160, are operatively coupled to the system bus 102. At least oneGraphics Processing Unit (GPU) 194 is operatively coupled to the systembus 102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present invention. The user input devices 152, 154,and 156 can be the same type of user input device or different types ofuser input devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. These and other variations of theprocessing system 100 are readily contemplated by one of ordinary skillin the art given the teachings of the present invention provided herein.

Moreover, it is to be appreciated that system 200 described below withrespect to FIG. 2 is a system for implementing respective embodiments ofthe present invention. Part or all of processing system 100 may beimplemented in one or more of the elements of system 200.

Further, it is to be appreciated that processing system 100 may performat least part of the method described herein including, for example, atleast part of method 300 of FIGS. 3-4 and/or at least part of method 500of FIGS. 5-6 and/or at least part of method 700 of FIG. 7. Similarly,part or all of system 200 may be used to perform at least part of method300 of FIGS. 3-4 and/or at least part of method 500 of FIGS. 5-6 and/orat least part of method 700 of FIG. 7.

FIG. 2 shows an exemplary system 200 to which the present invention canbe applied, in accordance with an embodiment of the present invention.The system 200 includes a computer processing system 210 (e.g., computerprocessing system 100) and a set of other computer processing systems220. In an embodiment, one or more of the computer processing systems210 and 220 can be configured as servers.

The computer processing system 210 can be configured to receive imagesfrom any of the other computer processing systems 220. The computerprocessing system 210 can subject the received images to a dataaugmentation technique for image classification tasks in accordance withthe present invention. The results of the data augmentation techniquecan then be provided from the computer processing system 210 to one ormore of the other computer processing systems 220. In this way, a moreaccurate image classification as compared to the prior art can beachieved in accordance with the teachings of the present invention.

Each of the computer processing system 210 and the other computerprocessing systems 220 at least include a processor 291, a memory 292,and a transceiver 293. Also, at least one of the computer processingsystems such as 210 can include a camera 299 for capturing an actualimage to which a trained neural network can be applied. Moreover, atleast the other computer processing systems 220 further include adatabase 294 for storing all or a portion of one or more training(image) sets. The transceiver 293 of the other computer processingsystems 220 send the images to the transceiver 293 of the computerprocessing system 210. The processor 291 and memory 292 of the computerprocessing system 210 then processing the images to provide an imageclassification result to one or more of the other computer processingsystems 220 via the transceivers 293.

In an embodiment, computer processing system 210 can be part of anothersystem 271. Such other system can be, for example, but is not limitedto, a surveillance system, a computer vision system, an actionrecognition system, an Advanced Driver-Assistance System, and so forth.It is to be appreciated that the preceding types of systems are merelyillustrative and the present invention can be applied to a myriad ofdifferent types of systems that can benefit from image classification.Other elements in these systems are not shown in FIG. 2 for the sake ofbrevity and clarity, but are nonetheless readily known and appreciatedby one of ordinary skill in the art.

In the embodiment shown in FIG. 2, the elements thereof areinterconnected by a network(s) 201. However, in other embodiments, othertypes of connections can also be used. Moreover, in an embodiment, atleast one of the elements of system 200 is processor-based (in the shownexample, all are processor-based). Further, while one or more elementsmay be shown as separate elements, in other embodiments, these elementscan be combined as one element. The converse is also applicable, wherewhile one or more elements may be part of another element, in otherembodiments, the one or more elements may be implemented as standaloneelements. Moreover, one or more elements of FIG. 2 can be implemented ina cloud configuration including, for example, in a distributedconfiguration. Additionally, one or more elements in FIG. 2 may beimplemented by a variety of devices, which include but are not limitedto, Digital Signal Processing (DSP) circuits, programmable processors,Application Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), and soforth. These and other variations of the elements of system 200 arereadily determined by one of ordinary skill in the art, given theteachings of the present invention provided herein, while maintainingthe spirit of the present invention.

Two exemplary methods 300 and 500 will now be described with respect toFIGS. 3-4 and 5-6. In particular, method 300 of FIGS. 3-4 corresponds toa data augmentation technique that is described using only two inputimages that form a single image pair for the sake of simplicity andillustration. In contrast, method 500 of FIGS. 5-6 corresponds to thedata augmentation technique of FIG. 3 applied to a set of input imagepairs that include more than one pair of input images. These and othervariations and extensions of methods 300 and 500 are readilycontemplated by one of ordinary skill in the art given the teachings ofthe present invention provided herein, while maintaining the spirit ofthe present invention. As mentioned above, any of system 100 and system200 can be used to perform method 300 and/or method 500 (and/or method700). To that end, it is to be appreciated that the method stepsdescribed herein can be performed by, e.g., a Central Processing Unit(CPU) (e.g., CPU 104 of FIG. 1) and/or by a Graphics Processing Unit(GPU) (GPU 194 of FIG. 1). It is to be further appreciated that whileone or more of the methods 300, 500, and 700 refer to the use of neuralnetworks, any type of machine learning process can be used in placethereof, as readily appreciated by one of ordinary skill in the art. Forexample, the present invention can be used with machine learningprocesses including, but not limited to, decision tree learning,association rule learning, deep learning, inductive programming logic,support vector machines, clustering, Bayesian networks, reinforcementlearning, representation learning, similarity and metric learning,sparse dictionary learning, rule-based machine learning, and learningclassification. The preceding machine learning processes to which thepresent invention can be applied are merely illustrative and, thus, thepresent invention can also be applied to other machine learningprocesses, while maintaining the spirit of the present invention.

FIGS. 3-4 show an exemplary method 300 for data augmentation for imageclassification tasks, in accordance with an embodiment of the presentinvention.

At step 305, select a first input image (hereinafter “first image”) anda second input image (hereinafter “second image”) from a training set.

At step 310, overlay the second image on the first image by averaging anintensity of each of a plurality of co-located pixel pairs in the firstand the second image to form a mixed image.

At step 315, train an image classifier, implemented as neural network,using the mixed image.

In an embodiment, step 315 can include one or more of steps 315A-C.

At step 315A, train the image classifier by using the label of the firstimage as a label of the mixed image.

At step 315B, train the image classifier by using the label of thesecond image as a label of the mixed image.

At step 315C, train the image classifier by mixing a label of the firstimage with a label of the second image to form a label of the mixedimage.

At step 320, continue training using the mixed image, for example, untila predetermined criteria has been met. The predetermined criteria caninclude, but is not limited to, for example, improvements in accuracy ortraining loss for training data or validation data. Thus, for example,training can be stopped when there is no further improvement inclassification accuracy by performing further training.

At step 325, perform fine tuning of the image classifier without mixingthe second image. That is, perform fine tuning using the unmixed images,i.e., the first image and the second image.

At step 330, receive an image to be classified.

At step 335, apply the trained neural network to the image to beclassified.

At step 340, output a classification for the image to be classified.

At step 345, perform an action in response (hereinafter interchangeablyreferred to as “response action”) to the classification for the image.The response action can be related to any number of applications towhich the present invention can be applied including, but not limitedto, surveillance, action recognition, an Advanced Driver-AssistanceSystem, computer vision, and so forth. Accordingly, the action can be,for example, generating an audible reproduction of the classification(e.g., in the case of computer vision), generating a user-perceptiblealert, locking a door to keep an object (e.g., a person or other animateobject and/or an inanimate object) contained, unlocking a door torelease a contained object, suggesting a correct action to a user (e.g.,responsive to a classification indicating an incorrect action is beingperformed by the user, e.g., in a documented procedure and/or otherprocess), taking control over one or more vehicle functions (e.g.,braking, accelerating, steering), and so forth. The classification cancorrespond to a prohibited and/or dangerous item and/or so forth. It isto be appreciated that the preceding response actions are merelyillustrative and, thus, any other response actions could also beperformed responsive to a classification made by the present invention,while maintaining the spirit of the present invention.

FIGS. 5-6 show another exemplary method 500 for data augmentation forimage classification tasks, in accordance with an embodiment of thepresent invention.

At step 505, select a set of one or more different input image pairsfrom a training set of images. Each of the image pairs in the setincludes a first respective input image (hereinafter “first image”) anda second respective input image (hereinafter “second image”) selectedfrom the set.

At step 510, for each image pair, overlay the second image on the firstimage by averaging an intensity of each of a plurality of co-locatedpixel pairs in the first and the second image to form a mixed image fromeach of the image pairs.

At step 515, train an image classifier, implemented as neural network,using the mixed images formed from each of the image pairs.

In an embodiment, step 515 can include one or more of steps 515A-C.

At step 515A, train the image classifier by using the label of the firstimage as a label of the mixed image.

At step 515B, train the image classifier by using the label of thesecond image as a label of the mixed image.

At step 515C, train the image classifier by mixing a label of the firstimage with a label of the second image to form a label of the mixedimage.

At step 520, continue training using the mixed images, for example,until a predetermined criteria has been met. The predetermined criteriacan include, but is not limited to, for example, improvements inaccuracy or training loss for training data or validation data. Thus,for example, training can be stopped when there is no furtherimprovement in classification accuracy by performing further training.

At step 525, perform fine tuning of the image classifier without mixingthe second images. That is, perform fine tuning using the unmixedimages, i.e., the first images and the second images of the image pairs.

At step 530, receive an image to be classified.

At step 535, apply the trained neural network to the image to beclassified.

At step 540, output a classification for the image to be classified.

At step 545, perform an action in response to the classification for theimage. Possible exemplary actions are further described above withrespect to step 345 of method 300.

FIG. 7 shows an overall neural network training scheme to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention.

At step 705, train the neural network in a training stage.

In an embodiment, step 705 can include one or more of steps 705A-C.

At step 705A, access a data augmentation technique to make the dataaugmentation technique available for use on an as needed or as calledfor basis. The data augmentation technique can be any of method 300 ofFIGS. 3-5 and method 500 of FIGS. 5-6.

At step 705B, disable the data augmentation technique at the beginningof the training stage, for example for a number of epochs. In this way,overall neural network training speed is increased and a better finalaccuracy can be obtained (as proven through numerous experiments).

At step 705C, disable the data augmentation technique at one or moreintermediate time periods of the training stage. For example, the dataaugmentation technique can be disabled for a number of epochs. In this,similar to step 710B, overall neural network training speed is increasedand a better final accuracy can be obtained.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8, illustrative cloud computing environment 850 isdepicted. As shown, cloud computing environment 850 includes one or morecloud computing nodes 810 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 854A, desktop computer 854B, laptop computer 854C,and/or automobile computer system 854N may communicate. Nodes 810 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 850 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 854A-Nshown in FIG. 8 are intended to be illustrative only and that computingnodes 810 and cloud computing environment 850 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 850 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 960 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 961;RISC (Reduced Instruction Set Computer) architecture based servers 962;servers 963; blade servers 964; storage devices 965; and networks andnetworking components 966. In some embodiments, software componentsinclude network application server software 967 and database software968.

Virtualization layer 970 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers971; virtual storage 972; virtual networks 973, including virtualprivate networks; virtual applications and operating systems 974; andvirtual clients 975.

In one example, management layer 980 may provide the functions describedbelow. Resource provisioning 981 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 982provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 983 provides access to the cloud computing environment forconsumers and system administrators. Service level management 984provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 985 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 990 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 991; software development and lifecycle management 992;virtual classroom education delivery 993; data analytics processing 994;transaction processing 995; and data augmentation for imageclassification tasks 996.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

1. A computer-implemented method for performing machine learning for animage classification task, the method comprising: selecting, by aprocessor operatively coupled to one or more databases, a first and asecond image from one or more training sets in the one or moredatabases; overlaying, by the processor, the second image on the firstimage to form a mixed image, by averaging an intensity of each of aplurality of co-located pixel pairs in the first and the second image;and training, by the processor, a machine learning process configuredfor the image classification task using the mixed image to augment dataused by the machine learning process for the image classification task.2. The computer-implemented method of claim 1, wherein said trainingstep comprises using a label of the first image as a label of the mixedimage.
 3. The computer-implemented method of claim 1, wherein saidtraining step comprises using a label of the second image as a label ofthe mixed image.
 4. The computer-implemented method of claim 1, whereinsaid training step comprises mixing a label of the first image and alabel of the second image to form a label of the mixed image.
 5. Thecomputer-implemented method of claim 1, wherein the selecting andoverlaying steps are repeated for each of a plurality of different imagepairs selected from the one or more training sets in the one or moredatabases to form a plurality of additional mixed images, and whereinthe machine learning process is trained using the plurality ofadditional mixed images.
 6. The computer-implemented method of claim 5,further comprising performing additional training without using anyfurther mixed images to increase a training speed of the machinelearning process.
 7. The computer-implemented method of claim 5, whereinthe additional training comprises a fine-tuning process for refining themachine learning process.
 8. The computer-implemented method of claim 1,wherein said selecting and overlaying steps are part of a dataaugmentation process used for training the machine learning process insaid training step, and wherein said data augmentation process isselectively disabled and enabled at one or more time periods to increasea training speed.
 9. The computer-implemented method of claim 8, whereinthe one or more time periods comprise multiple consecutive time periods,a first one of the multiple consecutive time periods corresponding to acommencement of a training stage for training the machine learningprocess.
 10. The computer-implemented method of claim 8, wherein the oneor more time periods are at intermediate periods in a training stage fortraining the machine learning process.
 11. The computer-implementedmethod of claim 1, wherein the processor is a graphics processing unit.12. The computer-implemented method of claim 1, wherein the imageclassification task relates to an advanced driver-assistance system andthe method further comprises: applying the trained machine learningprocess to a test image to obtain a classification for the test image;and controlling a function of one or more hardware devices of a motorvehicle, responsive to the classification for the test image. 13-25.(canceled)